Energy Modeling Test Preparation

Introduction

Energy investment banking modeling tests are more specialized than generalist tests, and candidates who prepare only with standard DCF and LBO practice models are often caught off guard by the energy-specific requirements. While not every energy bank administers a modeling test (some rely on technical interview questions instead), the larger banks and energy-focused boutiques (JPMorgan, Goldman Sachs, Citi, Tudor Pickering Holt, Petrie Partners, Evercore) may include a timed modeling exercise as part of the Superday process. Understanding what to expect and how to prepare gives you a significant advantage.

Energy modeling tests come in several formats, depending on the bank and the seniority of the role.

E&P NAV model (most common for analyst roles). You receive production data (current production, reserve volumes by category, decline rates or type curves), commodity price assumptions, operating cost data, and development capital requirements. Your task is to build a simplified NAV model that projects annual production, applies pricing, subtracts costs, discounts the cash flows, and arrives at a NAV per share. The test may ask you to present the NAV under multiple price scenarios.

Midstream DCF. You receive contracted revenue data (throughput volumes, fee rates, contract terms), operating expenses, capital expenditure plans (maintenance and growth), and the capital structure. Your task is to project distributable cash flow, calculate the coverage ratio, and value the company using a DCF with an exit multiple or yield-based terminal value.

Energy-adjusted LBO. A traditional LBO model with energy-specific modifications: the revenue line is driven by production volumes and commodity prices rather than revenue growth assumptions, the debt includes an RBL facility with a borrowing base that changes semiannually, and the exit valuation uses EV/EBITDAX rather than EV/EBITDA.

Case study with qualitative component. Some tests provide a company profile and ask you to build a simplified valuation and then prepare a brief investment recommendation. This format tests both modeling skills and the ability to synthesize data into actionable advice.

Modeling Test Time Constraints

Energy modeling tests typically allow 2-4 hours for completion. Analyst-level tests tend to be 2-3 hours and focus on building a model from a provided data set. Associate-level tests may extend to 3-4 hours and include both a quantitative model and a qualitative memo or recommendation. Unlike case interviews, modeling tests are usually completed independently on a provided laptop or emailed as a take-home exercise.

Three aspects of energy modeling tests differ from generalist banking tests, and candidates who miss these differences produce models that look wrong to energy interviewers.

Revenue is commodity-driven, not growth-rate-driven. In a generalist DCF, you project revenue using a growth rate applied to the prior year's revenue. In an energy model, revenue is a function of production volume (barrels, Mcf, or BOE) multiplied by commodity price per unit. You must build a production schedule (using decline curves or type curves) and a separate price deck, then multiply them to derive revenue. This two-input structure is more complex than a single growth rate but also more transparent.

The key metric is not EBITDA. Energy tests expect you to calculate EBITDAX for upstream models (adding back exploration expense) or distributable cash flow for midstream models (deducting only maintenance capex and interest, not growth capex). Using standard EBITDA in an E&P model signals that you do not understand energy-specific accounting.

Commodity price sensitivity is mandatory. A generalist DCF presents a base case with sensitivity on the discount rate and exit multiple. An energy model must include commodity price sensitivity as the primary variable, showing how the valuation changes across a range of oil and gas prices. If the test does not explicitly ask for this, add it anyway; it demonstrates energy-specific thinking.

Build Practice Models

The best preparation is building 2-3 practice energy models from scratch. Use publicly available data from E&P company investor presentations (which disclose type curves, production data, reserve volumes, and pricing) to build simplified NAV models. Resources like Breaking Into Wall Street, Wall Street Prep, and CFI offer energy-specific modeling courses that walk through the NAV framework step by step.

Practice NAV model. Download a mid-cap E&P investor presentation (Diamondback, Devon, or Coterra provide detailed type curve and reserve data). Build a simplified NAV: project PDP production using the company's disclosed decline rate, model 3-5 years of new PUD well development using the type curve, apply strip pricing, subtract LOE and drilling costs, and discount at 10%.

Practice midstream DCF. Use a midstream company's (Enterprise Products, Williams) 10-K to build a distributable cash flow projection: model fee-based revenue from disclosed throughput and rates, subtract operating expenses and maintenance capex, calculate coverage, and apply an exit yield for the terminal value.

Understand the Excel Techniques

Energy modeling requires certain Excel techniques that may not appear in generalist models. Decline curve calculations use formulas that multiply the prior year's production by (1 minus the decline rate): if Year 1 production is 500 barrels per day and the decline rate is 40%, Year 2 production is 500 * (1 - 0.40) = 300 barrels per day. For hyperbolic decline curves (where the decline rate itself decreases over time), you need the Arps decline equation, which incorporates a "b factor" that determines how quickly the decline rate moderates. Most modeling tests use simplified exponential decline for time efficiency, but know the concept of hyperbolic decline in case the test or interviewer asks.

Price deck structures should be built on a separate tab with the forward strip prices for each commodity (WTI, Henry Hub, Mont Belvieu ethane/propane/butane) for each projection year. The production tab then references the price deck, making it easy to run sensitivities by changing the price inputs without touching the production forecast.

Unit conversions between barrels, Mcf, MMBtu, and BOE are necessary when a company produces multiple products. Build a conversion row that translates gas production (in Mcf) to BOE using the 6:1 ratio, and NGL production (in barrels) directly to BOE. This ensures the aggregated production and per-unit metrics are consistent.

Master the Analytical Building Blocks

Even if you do not receive a full modeling test, the analytical concepts from energy models frequently appear in interview questions. Make sure you can: